Level-2 node clustering coefficient-based link prediction

Kumar, Ajay; Singh, Shashank Sheshar; Singh, Kuldeep; Biswas, Bhaskar

doi:10.1007/s10489-019-01413-8

Cited by 36 publications

(13 citation statements)

References 40 publications

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“…Clustering-based link prediction approaches have played a considerable role in enhancing the efficiency and accuracy of links inferencing. These methods have been discussed in several studies, for example, [11,67,21,27].…”

Section: Clustering-based Link-prediction Methodsmentioning

confidence: 99%

“…The approach proposed by [27] first involved extracting similar neighbors and grouping them into clusters, then computing the coefficients of the cluster up to two common levels. Their method exceeded the performance of all the alternative baseline methods.…”

Section: Clustering-based Link-prediction Methodsmentioning

confidence: 99%

“…A more interesting method for link prediction was proposed by [27], which employed the level-2 node clustering coefficient. As previously described, most link-prediction methods suffer from either lack of efficiency or accuracy.…”

Section: Predicting Links In Snmentioning

confidence: 99%

“…Plus, increased efficiency can lead to a reduction in accuracy. [27] suggested a method that was tested experimentally over 11 real-world datasets. The obtained results indicated that the method outperformed many of the stateof-the-art existing methods in terms of accuracy.…”

Section: Predicting Links In Snmentioning

confidence: 99%

“…The clusteringbased link-prediction methods use a node clustering coefficient to predict the link. The technique first computes the clustering coefficient then uses it to predict the future link [27].…”

Section: Techniques Used In Link Predictionmentioning

confidence: 99%

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Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies

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Section: Clustering-based Link-prediction Methodsmentioning

confidence: 99%

Section: Clustering-based Link-prediction Methodsmentioning

confidence: 99%

Section: Predicting Links In Snmentioning

confidence: 99%

Section: Predicting Links In Snmentioning

confidence: 99%

Section: Techniques Used In Link Predictionmentioning

confidence: 99%

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